Information processing method and terminal device

ABSTRACT

Disclosed are an information processing method and a terminal device. The method comprises: acquiring first information, wherein the first information is information to be processed by a terminal device; calling an operation instruction in a calculation apparatus to calculate the first information so as to obtain second information; and outputting the second information. By means of the examples in the present disclosure, a calculation apparatus of a terminal device can be used to call an operation instruction to process first information, so as to output second information of a target desired by a user, thereby improving the information processing efficiency. The present technical solution has advantages of a fast computation speed and high efficiency.

CROSS REFERENCE OF RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No. 16/760,235, filed on Apr. 29, 2020, which claims priority to U.S. National Phase Application No. PCT/CN2018/105463 filed on Sep. 13, 2018, the contents of the above-referenced applications are hereby expressly incorporated herein by reference in their entirety.

TECHNICAL FIELD

The present disclosure relates to the technical field of information technology, and particularly to an information processing method and related products.

BACKGROUND

With the growing information technology and people's ever-increasing demand, the need for timeliness of information becomes stronger. At present, terminal devices obtain information by general-purpose processors. For instance, a general-purpose processor may run a specified application to implement object detection and classification, or to implement identification of the current environment, etc.

However, in practical applications, this way of obtaining information by a general-purpose processor running a software program may be limited by the operating speed of the general-purpose processor, and in particular, when the general-purpose processor has a large load, the efficiency of obtaining information may be low and the delay may be long.

SUMMARY

Examples of the present disclosure provide an information computation method and related products, which can increase processing speed and efficiency of a computation device.

In a first aspect, an example of the present disclosure provides an information processing method which is applied to a computation device, where the computation device includes a communication unit and an operation unit. The method includes:

-   -   controlling, by the computation device, the communication unit         to obtain a target image to be processed, where the target image         includes a target object to be identified;     -   controlling, by the computation device, the operation unit to         obtain and call an operation instruction to perform object         detection and identification on the target image, so as to         obtain a target detection result, where     -   the target detection result is used to indicate the target         object in the target image, and the operation instruction is a         pre-stored instruction for object detection and identification.

In some possible examples, the computation device further includes a register unit and a controller unit, and the controlling, by the computation device, the operation unit to obtain an operation instruction to perform object detection and identification on the target image, so as to obtain a target detection result includes:

-   -   controlling, by the computation device, the controller unit to         fetch a first operation instruction and a second operation         instruction from the register unit, and sending, by the         computation device, the first operation instruction and the         second operation instruction to the operation unit;     -   controlling, by the computation device, the operation unit to         call the first operation instruction to perform feature         extraction on the target image to obtain a feature image; and     -   controlling, by the computation device, the operation unit to         call the operation instruction to perform object detection and         identification on the feature image, so as to obtain a target         detection result, where     -   the first operation instruction is a preset instruction for         feature extraction, and the second operation instruction is a         preset instruction for object detection and identification.

In some possible examples, the calling the first operation instruction to perform feature extraction on the target image to obtain a feature image includes:

-   -   controlling, by the computation device, the operation unit to         perform feature extraction on the target image based on an         operation instruction set of at least one thread to obtain a         feature image, where the operation instruction set includes at         least one first operation instructions, and an order of calling         the first operation instruction in the operation instruction set         is customized by a user side or a terminal side.

In some possible examples, the target detection result includes target information corresponding to the target object, and the target information includes at least one of the following: a target bounding box, a target classification, and a target score, where

-   -   the target bounding box includes the target object and is used         to indicate a position of the target object in the target image,         the target classification is a classification to which the         target object belongs, and the target score is used to indicate         an accuracy of the target bounding box bounding the target         object.

In some possible examples, the target detection result includes: a target bounding box, a target classification, and a target score, and the calling the operation instruction to perform object detection and identification on the target image, so as to obtain a detection result includes:

-   -   controlling, by the computation device, the operation unit to         call the second operation instruction to perform object         detection and identification on the feature image, so as to         obtain n detection results for the target object, where the         detection results include a rounding box, a classification, and         a score corresponding to the target object, and     -   controlling, by the computation device, the operation unit to         select a detection result with a highest score from the n         detection results as the target detection result.

In some possible examples, the controlling, by the computation device, the communication unit to obtain a target image to be processed includes:

-   -   controlling, by the computation device, the communication unit         to obtain an original image to be processed input by a user; and     -   controlling, by the computation device, the operation unit to         pre-process the original image to obtain a target image to be         processed, where the pre-processing includes one or more of the         following processing: format conversion, normalization, color         conversion, and image restoration.

In some possible examples, the computation device further includes a data access unit and a storage medium,

-   -   the computation device controls the operation unit to send the         target detection result to the data access unit and store the         result in the storage medium.

In some possible examples, the operation unit includes a primary operation module and a plurality of secondary operation modules, where the primary operation module is interconnected with the plurality of secondary operation modules by an interconnection module, and when the operation instruction is a convolution operation instruction,

-   -   the calling the operation instruction to perform object         detection and identification on the target image includes:     -   controlling, by the computation device, the secondary operation         modules to implement a convolution operation of input data and a         convolution kernel in a convolutional neural network algorithm,         where the input data is the target image and the convolutional         neural network algorithm corresponds to the convolution         operation instruction;     -   controlling, by the computation device, the interconnection         module to implement data transfer between the primary operation         module and the secondary operation modules, before a forward         operation of a neural network fully connected layer starts,         transferring, by the primary operation module, the input data to         each secondary operation module through the interconnection         module; and after the computation of the secondary operation         modules is completed, splicing, by the interconnection module,         output scalars of the respective secondary operation modules         stage by stage to obtain an intermediate vector, and sending the         intermediate vector back to the primary operation module, and     -   controlling, by the computation device, the primary operation         module to splice intermediate vectors corresponding to all input         data into an intermediate result for subsequent operations.

In some possible examples, the performing subsequent operations on the intermediate result includes:

-   -   controlling, by the computation device, the primary operation         module to add bias data to the intermediate result, and then         performing an activation operation.

In some possible examples, the primary operation module includes a first operation unit, where the first operation unit includes a vector addition unit and an activation unit,

-   -   the steps of controlling, by the computation device, the primary         operation module to add bias data to the intermediate result,         and then performing an activation operation include:     -   controlling, by the computation device, the vector addition unit         to implement a bias addition operation of a convolutional neural         network operation and perform element-wise addition on bias data         and the intermediate result to obtain a bias result; and     -   controlling, by the computation device, the activation unit to         perform an activation function operation on the bias result.

In some possible examples, the primary operation module includes a first storage unit, a first operation unit, a first data dependency determination unit, and a first storage unit;

-   -   the computation device controls the first storage unit to cache         input data and output data used by the primary operation module         during a computation process, where the output data includes the         object detection result,     -   the computation device controls the first operation unit to         perform various operational functions of the primary operation         module,     -   the computation device controls the data dependency         determination unit to ensure that there is no consistency         conflict in reading data from and writing data to the first         storage unit, read an input neuron vector from the first storage         unit, and send the vector to the secondary operation modules         through the interconnection module; and     -   sending an intermediate result vector from the interconnection         module to the first operation unit.

In some possible examples, each of the secondary operation modules includes a second operation unit, where the second operation unit includes a vector multiplication unit and an accumulation unit,

-   -   the controlling, by the computation device, the secondary         operation modules to perform a convolution operation of input         data and a convolution kernel in a convolutional neural network         algorithm includes:     -   controlling, by the computation device, the vector         multiplication unit to perform a vector multiplication operation         of the convolution operation, and     -   controlling, by the computation device, the accumulation unit to         perform an accumulation operation of the convolution operation.

In some possible examples, each secondary operation module includes a second operation unit, a second data dependency determination unit, a second storage unit, and a third storage unit. The above method includes:

-   -   controlling, by the computation device, the second operation         unit to perform various arithmetic and logical operations of the         secondary operation modules,     -   controlling, by the computation device, the second data         dependency determination unit to perform a reading/writing         operation on the second storage unit and the third storage unit         during a computation process and ensure that there is no         consistency conflict between the reading and writing operations         on the second storage unit and the third storage unit,     -   controlling, by the computation device, the second storage unit         to cache input data and an output scalar and an output scalar         obtained from the computation performed by the secondary         operation module, and     -   controlling, by the computation device, the third storage unit         to cache a convolution kernel required by the secondary         operation module during the computation process.

In some possible examples, the first data dependency or the second data dependency ensures that there is no consistency conflict in reading and writing in the following manners: storage addresses corresponding to data/instructions stored in the corresponding storage unit do not overlap; or determining whether there is dependency between a control signal that has not been executed and data of a control signal that is being executed, if there is no dependency, the control signal is allowed to be issued immediately, otherwise, the control signal is not allowed to be issued until all control signals on which the control signal is dependent have been executed, where

-   -   the computation device controls the controller unit to obtain an         operation instruction from the register unit and decode the         operation instruction into the control signal for controlling         behavior of other modules, where the other modules include the         main the primary operation module and the plurality of secondary         operation modules.

In some possible examples, the computation device controls the plurality of secondary operation modules to compute respective output scalars in parallel by using the same input data and respective convolution kernels.

In some possible examples, an activation function active used by the primary operation module may be any of the following non-linear functions: sigmoid, tanh, relu, softmax, or may be a linear function.

In some possible examples, the interconnection module forms a data channel for continuous or discrete data between the primary operation module and the plurality of secondary operation modules. The interconnection module has any of the following structures: a tree structure, a ring structure, a grid structure, a hierarchical interconnection, and a bus structure.

In a second aspect, an example of the present disclosure provides a computation device which includes a function unit configured to perform the methods of the first aspect.

In a third aspect, an example of the present disclosure provides a computer readable storage medium on which a computer program used for electronic data exchange is stored, where the computer program enables a computer to perform the methods of the first aspect.

In a fourth aspect, an example of the present disclosure further provides a computer program product which includes a non-transitory computer readable storage medium storing a computer program. The computer program may cause a computer to perform the methods of the first aspect.

In a fifth aspect, an example of the present disclosure provides a chip which includes the computation device of the second aspect.

In a sixth aspect, an example of the present disclosure provides a chip package structure which includes the chip of the fifth aspect.

In a seventh aspect, an example of the present disclosure provides a board card which includes the chip package structure of the sixth aspect.

In an eighth aspect, an example of the present disclosure provides an electronic device which includes the board card of the seventh aspect.

In some examples, the electronic device includes a data processing device, a robot, a computer, a printer, a scanner, a tablet, a smart terminal, a mobile phone, a traffic recorder, a navigator, a sensor, a webcam, a server, a cloud-based server, a camera, a video camera, a projector, a watch, a headphone, a mobile storage, a wearable device, a vehicle, a household appliance, and/or a medical equipment.

In some examples, the vehicle includes an airplane, a ship, and/or a car. The household electrical appliance includes a television, an air conditioner, a microwave oven, a refrigerator, a rice cooker, a humidifier, a washing machine, an electric lamp, a gas cooker, and a range hood. The medical equipment includes a nuclear magnetic resonance spectrometer, a B-ultrasonic scanner, and/or an electrocardiograph.

Technical effects of implementing the examples of the present disclosure are as follows:

-   -   it can be seen that through the examples of the present         disclosure, a computation device may control a communication         unit to obtain a target image to be processed, where the target         image includes a target object to be identified, and then the         computation device controls an operation unit to call an         operation instruction to perform object detection and         identification on the target image, so as to obtain a target         identification result, where the target detection result is used         to indicate the target object in the target image, and the         operation instruction is a pre-stored instruction for object         detection and identification; in this way, a target image to be         identified in the target image may be accurately, quickly, and         comprehensively detected and identified; compared with the prior         art which uses general-purpose processors to implement detection         and identification, the examples of the present disclosure has         beneficial effects of lower power consumption and faster speed.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to illustrate the technical solutions in the examples of the present disclosure more clearly, the drawings to be used in the description of the examples are briefly explained below. Obviously, the drawings in the description below are some examples of the present disclosure. Other drawings can be obtained according to the disclosed drawings without any creative effort by those skilled in the art.

FIG. 1A is a structural diagram of a computation device according to an example of the present disclosure.

FIG. 1B is a schematic flowchart of a convolutional neural network algorithm.

FIG. 1C is a schematic diagram of an instruction of a device supporting a convolutional neural network forward operation according to an example of the present disclosure.

FIG. 1D is a block diagram of an overall structure of a device for performing a convolutional neural network forward operation according to an example of the present disclosure.

FIG. 1E is a structural diagram of an H-tree module (an implementation of an interconnection module) of a device for performing a convolutional neural network forward operation according to an example of the present disclosure.

FIG. 1F is a block diagram of a structure of a primary operation module of a device for performing a convolutional neural network forward operation according to an example of the present disclosure.

FIG. 1G is a block diagram of a structure of a secondary operation module of a device for performing a convolutional neural network forward operation according to an example of the present disclosure.

FIG. 1H is a block diagram of a process of a single-layer convolutional neural network forward operation according to an example of the present disclosure.

FIG. 2 is a flowchart of an information processing method according to an example of the present disclosure.

FIG. 3 is a schematic diagram of calling an operation instruction based on single-thread according to an example of the present disclosure.

FIG. 4 is a schematic diagram of calling an operation instruction based on multiple threads according to an example of the present disclosure.

FIG. 5 is a flowchart of a target bounding box according to an example of the present disclosure.

FIG. 6 is a structural diagram of another computation device according to an example of the present disclosure.

DETAILED DESCRIPTION OF THE EXAMPLES

Technical solutions in examples of the present disclosure will be described clearly and completely hereinafter with reference to the accompanied drawings in the examples of the present disclosure. Obviously, the examples to be described are merely some rather than all examples of the present disclosure. All other examples obtained by those of ordinary skill in the art based on the examples of the present disclosure without creative efforts shall fall within the protection scope of the present disclosure.

Terms such as “first”, “second”, “third”, and “fourth” in the specification, the claims, and the drawings are used for distinguishing different objects rather than describing a specific order. In addition, terms such as “include”, “have”, and any variant thereof are used for indicating non-exclusive inclusion. For instance, a process, a method, a system, a product, or an equipment including a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to the process, the method, the product, or the equipment.

Reference to “example” means that a particular feature, a structure, or a characteristic described in conjunction with the example may be included in at least one example of the present disclosure. The term used in various places in the specification does not necessarily refer to the same example, nor does it refer to an example that is mutually exclusive, independent, or alternative to other examples. It can be explicitly and implicitly understood by those skilled in the art that the examples described herein may be combined with other examples.

First, a computation device used in the present disclosure is introduced. FIG. 1A provides a computation device, where the device includes a storage medium 611 (optional), a register unit 612, an interconnection module 613, an operation unit 614, a controller unit 615, and a data access unit 616, where

-   -   the operation unit 614 include at least two of the following: an         addition arithmetic unit, a multiplication arithmetic unit, a         comparator, and an activation arithmetic unit.

The interconnection module 613 is configured to control a connection relationship of the arithmetic units in the operation unit 614 so that the at least two arithmetic units form a different computation topology.

The instruction storage unit (which may be a register unit, an instruction cache, or a scratchpad memory) 612 is configured to store the operation instruction, an address of a data block in the storage medium, and a computation topology corresponding to the operation instruction.

The operation instruction may include an operation field and an opcode. Taking a convolution operation instruction as an example, as shown in Table 1, register 0, register 1, register 2, register 3, and register 4 may be operation fields. Each of the register 0, register 1, register 2, register 3, and register 4 may be one or a plurality of registers.

Regis- Opcode Register 0 ter 1 Register 2 Register 3 Register 4 COM- Input data Input Convolu- Convolu- Address of PUTE starting data tion tion an activation address length kernel kernel function starting length interpolation address table IO Address of Data Address of an external length an internal memory of memory of data data NOP JUMP Target address MOVE Input Data Output address size address

The storage medium 611 may be an off-chip memory, and in certain applications; may also be an on-chip memory for storing a data block. The data block may be n-dimensional data, where n is an integer greater than or equal to 1. For instance, when n=1, the data is one-dimensional data, which is a vector; when n=2, the data is two-dimensional data, which is a matrix; and when n is equal to or greater than 3, the data is multi-dimensional data.

The control unit 615 is configured to fetch an operation instruction, an operation field corresponding to the operation instruction, and a first computation topology corresponding to the operation instruction from the register unit 612, and decode the operation instruction into an execution instruction. The execution instruction is configured to control the operation unit to perform an operation, transfer the operation field to the data access unit 616, and transfer the computation topology to the interconnection module 613.

The data access unit 616 is configured to fetch a data block corresponding to the operation field from the storage medium 611 and transfer the data block to the interconnection module 613.

The interconnection module 613 is configured to receive the first computation topology and the data block. In an example, the interconnection module 613 is further configured to rearrange the data block according to the first computation topology.

The operation unit 614 is configured to call an arithmetic unit of the operation unit 614 according to the execution instruction to perform an operation on the data block to obtain an operation result, transfer the operation result to the data access unit, and store the result in the storage medium. In an example, the operation unit 614 is configured to call an arithmetic unit according to the first computation topology and the execution instruction to perform an operation on the rearranged data block to obtain an operation result, transfer the operation result to the data access unit, and store the result in the storage medium.

In another example, the interconnection module 613 is configured to form the first computation topology according to the connection relationships of the arithmetic units in the operation unit 614.

An interconnection module is set in the computation device provided by the present disclosure. The interconnecting module can connect the arithmetic units in the computation unit to obtain a computation topology corresponding to the computation instruction according to the needs of the computation instruction, so that there is no need to store or fetch intermediate data of the computation in subsequent operations of the operation unit. Through this structure, a single instruction can implement a single input and perform operations of a plurality of arithmetic units to obtain a computation result, which improves the computation efficiency.

A computation method of the computation device shown in FIG. 1A is explained below based on different operation instructions. As an instance, the operation instruction may be a convolution operation instruction. The convolution operation instruction can be applied to a neural network, so the convolution operation instruction may also be called a convolutional neural network operation instruction. A formula to be perform by the convolution operation instruction may be: s=s(Σwx_(i)+b) which is to multiply a convolution kernel w by input data x_(i), find the sum, add a bias b, and then perform an activation operation s(h) to obtain a final output result S. According to the formula, the computation topology may be obtained, which is: the multiplication arithmetic unit—the addition arithmetic unit—the (optional) activation arithmetic unit.

A method of performing a convolution operation instruction by the computation device shown in FIG. 1A may include:

-   -   fetching, by the control unit 615, a convolution operation         instruction, an operation field corresponding to the convolution         operation instruction, and the first computation topology (the         multiplication arithmetic unit—the addition arithmetic unit—the         addition arithmetic unit—the activation arithmetic unit)         corresponding to the convolution operation instruction from the         register unit 612; transferring, by the control unit, the         operation field to a data access unit, and transferring the         first computation topology to the interconnection module;     -   fetching, by the data access unit, a convolution kernel w and a         bias b (if b is 0, there is no need to fetch the bias b)         corresponding to the operation field from the storage medium,         and transferring the convolution kernel w and the bias b to the         operation unit; and     -   multiplying, by the multiplication arithmetic unit of the         computation unit, a convolution kernel w and input data Xi to         obtain a first result, inputting the first result to the         addition arithmetic unit to perform addition to obtain a second         result, adding the second result and a bias b to obtain a third         result, inputting the third result to the activation arithmetic         unit to perform an activation operation to obtain an output         result S, transferring the output result S to the data access         unit, and storing, by the data access unit, the output result in         the storage medium. After each step, the result may be         transferred to the data access and stored in storage medium         without performing a following step. The step of adding the         second result and the bias b to obtain the third result is         optional, which means this step is not required when b is 0.

In addition, the order of addition and multiplication can be reversed.

The technical solution provided by the present disclosure can realize convolution operations according to one instruction which is a convolution operation instruction. There is no need to store or obtain intermediate data of convolution operations (such as a first result, a second result, and a third result). The technical solution may reduce the storing and obtaining operations of intermediate data, and may have technical effects of reducing a corresponding operation step and improving outcomes of convolution operations.

It should be understood that the instruction set used in the present disclosure may include one or a plurality of operation instructions. The operation instruction includes, but is not limited to a COMPUTE instruction (an operation instruction), a CONFIG instruction, an IO instruction, an NOP instruction, a JUMP instruction, a MOVE instruction, etc. The COMPUTE instruction includes, but is not limited to, a convolution (CONV) instruction, a pooling operation instruction, etc. Specifically, an executable computation instruction in the present disclosure includes:

-   -   a convolution operation instruction. In an example, the         convolution COMPUTE instruction (the CONV instruction) includes:     -   a convolutional neural network sigmoid instruction: according to         the instruction, a device fetches input data and a convolution         kernel of a specified size from a specified address in a memory         (optionally a scratchpad memory or a scalar register file),         performs a convolution operation in a convolution operation         component, and optionally, performs sigmoid activation on an         output result;     -   a convolutional neural network TanH instruction: according to         the instruction, the device fetches input data and a convolution         kernel of a specified size from a specified address in a memory         (optionally a scratchpad memory) respectively, performs a         convolution operation in the convolution operation component,         and then performs TanH activation on an output result;     -   a convolutional neural network ReLU instruction: according to         the instruction, the device fetches input data and a convolution         kernel of a specified size from a specified address in the         memory (optionally a scratchpad memory) respectively, performs a         convolution operation in a convolution operation component, and         then performs ReLU activation on an output result; and     -   a convolutional neural network group instruction: according to         the instruction, the device fetches input data and a convolution         kernel of a specified size from a specified address in the         memory (optionally a scratchpad memory) respectively, partitions         the input data and the convolution kernel into groups, performs         a convolution operation in a convolution operation component,         and then performs activation on an output result.

A convolution operation instruction (pure convolution operation instruction): according to the instruction, the device fetches input data and a convolution kernel of a specified size from a specified address in the memory (optionally a scratchpad memory) respectively, and performs a convolution operation in a convolution operation component. The above-mentioned specified size may be set by the user or manufacturer. For instance, in a computation device of a first manufacturer, the specified size may be set to data of A bit, and in a computation device of a second manufacturer, the specified size may be set to data of B bit. The data of A bit and the data of B bit have different sizes.

The pooling instruction. In an example, the pooling COMPUTE instruction (the pooling operation instruction, which is also referred to as the pooling instruction in the present disclosure) specifically includes:

-   -   a Maxpooling forward operation instruction: according to the         instruction, the device fetches input data of a specified size         from a specified address in a memory (optionally a scratchpad         memory or a scalar register file), performs a Maxpooling forward         operation in a pooling operation component, and writes a         computation result back to a specified address in the memory         (optionally a scratchpad memory or a scalar register file);     -   a Maxpooling backward training instruction: according to the         instruction, the device fetches input data of a specified size         from a specified address in a memory (optionally a scratchpad         memory or a scalar register file), performs Maxpooling backward         training in a pooling operation component, and writes a         computation result back to a specified address in the memory         (optionally a scratchpad memory or a scalar register file);     -   an Avgpooling forward operation instruction: according to the         instruction, the device fetches input data of a specified size         from a specified address in a memory (optionally a scratchpad         memory or a scalar register file), performs an Avgpooling         forward operation in a pooling operation component, and writes a         computation result back to a specified address in the memory         (optionally a scratchpad memory or a scalar register file);     -   an Avgpooling backward training instruction: according to the         instruction, the device fetches input data of a specified size         from a specified address in a memory (optionally a scratchpad         memory or a scalar register file), performs Avgpooling backward         training in a pooling operation component, and writes a         computation result back to a specified address in the memory         (optionally a scratchpad memory or a scalar register file);     -   a Minpooling forward operation instruction: according to the         instruction, the device fetches input data of a specified size         from a specified address in a memory (optionally a scratchpad         memory or a scalar register file), performs a Minpooling forward         operation in a pooling operation component, and writes a         computation result back to a specified address in the memory         (optionally a scratchpad memory or a scalar register file); and     -   a Minpooling backward training instruction: according to the         instruction, the device fetches input data of a specified size         from a specified address in a memory (optionally a scratchpad         memory or a scalar register file), performs Minpooling backward         training in a pooling operation component, and writes a         computation result back to a specified address in the memory         (optionally a scratchpad memory or a scalar register file).

A batch normalization instruction can be used for a batch normalization computation.

A fully connected instruction may include a fully connected layer forward operation instruction.

A fully connected layer forward operation instruction: according to the instruction, a device fetches weight data and bias data from a specified address in a memory, performs a full connection operation in a computation unit, and writes a computation result back to a specified address in a scratchpad memory.

The CONFIG instruction configures various constants required by a computation of a current artificial neural network layer before the computation starts. For instance, 1/kernel_area can be obtained by configuration using the CONFIG instruction. In the batch normalization computation, the CONFIG instruction configures various constants required for a current layer before a batch normalization computation begins.

The IO instruction is for reading-in input data required for a computation from an external storage space, and storing data to the external space after the computation finishes.

The NOP instruction is for emptying control signals in all control signal cache queues in the current device, and ensuring that all instructions before the NOP instruction are finished. The NOP instruction itself does not include any operations.

The JUMP instruction is for controlling jumping of a next instruction address to be read from an instruction storage unit, so that the jumping of a control flow can be realized.

The MOVE instruction is for moving data of an address in an internal address space of the device to another address in the internal address space of the device. This process is independent of an operation unit and does not occupy resources of the operation unit during execution.

Optionally, operation instructions that can be executed by the computation device may further include:

-   -   a Matrix Mult Vector (MMV) instruction: according to the         instruction, the device fetches matrix data and vector data of a         set length from a specified address in a scratchpad memory,         performs a matrix-multiply-vector operation in the operation         unit, and writes a computation result back to a specified         address in the scratchpad memory; it is worth noting that a         vector can be stored in the scratchpad memory as a matrix of a         special form (a matrix with only one row of elements);     -   a Vector Mult Matrix (VMM) instruction: according to the         instruction, the device fetches vector data and matrix data of a         set length from a specified address in a scratchpad memory,         performs a vector-multiply-matrix operation in the operation         unit, and writes a computation result back to a specified         address in the scratchpad memory; it is worth noting that a         vector can be stored in the scratchpad memory as a matrix of a         special form (a matrix with only one row of elements);     -   a Matrix Mult Scalar (VMS) instruction: according from         instruction, the device fetches matrix data of a set length from         a specified address in a scratchpad memory, fetches matrix data         of a specified size from a specified address of a scalar         register file, and performs a scalar-multiply-matrix operation         in the operation unit, and writes a computation result back to a         specified address in the scratchpad memory; it is worth noting         that the scalar register file stores not only an address of the         matrix but also scalar data;     -   a Tensor Operation (TENS) instruction: according to the         instruction, the device fetches two pieces of matrix data of a         set length from two specified addresses in a scratchpad memory,         performs a tensor operation on the two pieces of matrix data in         the operation unit, and writes a computation result back to a         specified address of the scratchpad memory;     -   a Matrix Add Matrix (MA) instruction: according to the         instruction, the device fetches two pieces of matrix data of a         set length from two specified addresses in a scratchpad memory,         adds the two pieces of matrix data in the operation unit, and         writes a computation result back to a specified address in the         scratchpad memory;     -   a Matrix Sub Matrix (MS) instruction: according to the         instruction, the device fetches two pieces of matrix data of a         set length from two specified addresses in a scratchpad memory,         performs a subtraction operation on the two pieces of matrix         data in the operation unit, and writes a computation result back         to a specified address in the scratchpad memory;     -   a Matrix Retrieval (MR) instruction: according to the         instruction, the device fetches vector data of a set length from         a specified address in a scratchpad memory, fetches matrix data         of a specified size from a specified address in the scratchpad         memory; in the operation unit, the vector is an index vector,         and an i^(th) element of an output vector is a number obtained         from an i^(th) column of the matrix by using an i^(th) element         of the index vector as an index; and the output vector is         written back to a specified address in the scratchpad memory;     -   a Matrix Load (ML) instruction: according to the instruction,         the device loads data of a set length from a specified external         source address to a specified address in a scratchpad memory;     -   a Matrix Store (MS) instruction: according to the instruction,         the device stores matrix data of a set length from a specified         address in a scratchpad memory to an external target address;     -   a Matrix Move (MMOVE) instruction: according to the instruction,         the device moves matrix data of a set length from a specified         address in a scratchpad memory to another specified address in         the scratchpad memory;     -   a Vector-Inner-Product instruction (VP): according to the         instruction, the device fetches vector data of a specified size         from a specified address in a memory (optionally a scratchpad         memory or a scalar register file), performs an inner product (a         scalar) on two vectors in a vector computation unit, and writes         the result back; optionally, the result is written back to a         specified address in the memory (optionally a scratchpad memory         or a scalar register file);     -   a vector cross product instruction (TENS): according to the         instruction, the device fetches vector data of a specified size         from a specified address in a memory (optionally a scratchpad         memory or a scalar register file), performs an inner product (a         scalar) on two vectors in a vector computation unit, and writes         the result back; optionally, the result is written back to a         specified address in the memory (optionally a scratchpad memory         or a scalar register file);     -   a vector elementary arithmetic operation including a         Vector-Add-Scalar instruction (VAS): according to the         instruction, the device fetches vector data of a specified size         from a specified address in a memory (optionally a scratchpad         memory or a scalar register file), fetches scalar data from a         specified address of a scalar register file of the memory, adds         the scalar to each element of the vector in a scalar computation         unit, and writes the result back; optionally, the result is         written back to a specified address in the memory (optionally a         scratchpad memory or a scalar register file);     -   a Scalar-Sub-Vector instruction (SSV): according to the         instruction, the device fetches scalar data from a specified         address in the scalar register in a memory (optionally a         scratchpad memory or a scalar register file), fetches vector         data from a specified address in the memory (optionally the         scratchpad memory or the scalar register file), subtracts         corresponding elements of the vector from the scalar in a vector         computation unit, and writes the result back; optionally, the         result is written back to a specified address in the memory         (optionally a scratchpad memory or a scalar register file);     -   a Vector-Dev-Vector instruction (VD): according to the         instruction, the device fetches vector data of a specified size         from a specified address in a memory (optionally a scratchpad         memory or a scalar register file), performs an element-wise         division of two vectors in a vector computation unit, and writes         the result back; optionally, the result is written back to a         specified address in the memory (optionally a scratchpad memory         or a scalar register file);     -   a Scalar-Dev-Vector instruction (SDV): according to the         instruction, the device fetches scalar data from a specified         address in the scalar register file of a memory (optionally a         scratchpad memory or a scalar register file), fetches vector         data of a specified size from a specified address in the memory         (optionally the scratchpad memory), divides the scalar by         corresponding elements in the vector in a vector computation         unit, and writes the result back; optionally, the result is         written back to a specified position in the memory (optionally a         scratchpad memory or a scalar register file).

The computation device can also execute a vector logic instruction, including:

-   -   a Vector-AND-Vector instruction (VAV): according to the         instruction, the device fetches vector data of a specified size         from a specified address in a memory (optionally a scratchpad         memory or a scalar register file) respectively, performs an         element-wise AND on two vectors in a vector computation unit,         and writes the result back; optionally, the result is written         back to a specified address in the memory (optionally a         scratchpad memory or a scalar register file);     -   a Vector-AND instruction (VAND): according to the instruction,         the device fetches vector data of a specified size from a         specified address in a memory (optionally a scratchpad memory or         a scalar register file), performs an element-wise AND operation         on two vectors in a vector computation unit, and writes the         result back; optionally, the result is written back to a         specified address in the scalar register file of the memory         (optionally a scratchpad memory or a scalar register file);     -   a Vector-OR-Vector instruction (VOV): according to the         instruction, the device fetches vector data of a specified size         from a specified address in a memory (optionally a scratchpad         memory) respectively, performs an element-wise OR operation on         two vectors in a vector computation unit, and writes the result         back; optionally, the result is written back to a specified         address in the memory (optionally a scratchpad memory or a         scalar register file);     -   a Vector-OR instruction (VOR): according to the instruction, the         device fetches vector data of a specified size from a specified         address in a memory (optionally a scratchpad memory or a scalar         register file), performs an OR operation on each element of the         vector in a vector computation unit, and writes the result back;         optionally, the result is written back to a specified address in         the scalar register file of the memory (optionally a scratchpad         memory or a scalar register file);     -   a transcendental function instruction: according to the         instruction, the device fetches vector data of a specified size         from a specified address in a memory (optionally a scratchpad         memory or a scalar register file), performs a transcendental         function operation on the vector data in an operation unit, and         writes the result back; optionally, the result is written back         to a specified address in a storage unit of the memory         (optionally a scratchpad memory or a scalar register file).         Optionally, the result is written back specified address in the         memory (optionally a scratchpad memory or a scalar register         file);

The computation device can also execute a vector comparison operation instruction, including:

-   -   a Greater-Equal operation instruction (GE): according to the         instruction, the device may obtain parameters of the         instruction, including a length of a vector, a starting address         of two vectors, and a storage address of an output vector,         directly from the instruction or by accessing the serial number         of the register of a memory (optionally a scratchpad memory or a         scalar register file) provided by the instruction, then read         data of the two vectors, and compare the elements at all         positions in the vectors in a vector comparison operation unit;         at the position of a row, if the value of a previous vector is         greater than or equal to the value of a subsequent vector, the         value of the comparison result vector at that position is set to         1, otherwise it is set to 0; finally, the comparison result is         written back to a specified storage address in the memory         (optionally the scratchpad memory or the scalar register file);     -   a Less-Equal operation instruction (LE): according to the         instruction, the device may obtain the parameters of the         instruction, including the length of a vector, the starting         address of the two vectors, and the storage address of the         output vector, directly from the instruction or by accessing the         serial number of the register of a memory (optionally a         scratchpad memory or a scalar register file) provided by the         instruction, then read the data of the two vectors, and compare         the elements at all positions in the vectors in a vector         comparison operation unit; at the position of a row, if the         value of a previous vector is less than or equal to the value of         a subsequent vector, the value of the comparison result vector         at that position is set to 1, otherwise it is set to 0; finally,         the comparison result is written back to a specified storage         address in the memory (optionally the scratchpad memory or the         scalar register file);     -   a Greater-Than operation instruction (GT): according to the         instruction, the device may obtain the parameters of the         instruction, including the length of a vector, the starting         address of the two vectors, and the storage address of the         output vector, directly from the instruction or by accessing the         serial number of the register of a memory (optionally a         scratchpad memory or a scalar register file) provided by the         instruction, then read the data of the two vectors, and compare         the elements at all positions in the vectors in a vector         comparison operation unit; at the position of a row, if the         value of a previous vector is greater than the value of a         subsequent vector, the value of the comparison result vector at         that position is set to 1, otherwise it is set to 0; finally,         the comparison result is written back to a specified storage         address in the memory (optionally the scratchpad memory or the         scalar register file);     -   a Less-Than operation instruction (LT): according to the         instruction, the device may obtain the parameters of the         instruction, including the length of a vector, the starting         address of the two vectors, and the storage address of the         output vector, directly from the instruction or by accessing the         serial number of the register of a memory (optionally a         scratchpad memory or a scalar register file) provided by the         instruction, then read the data of the two vectors, and compare         the elements at all positions in the vectors in a vector         comparison operation unit; at the position of a row, if the         value of a previous vector is less than the value of a         subsequent vector, the value of the comparison result vector at         that position is set to 1, otherwise it is set to 0; finally,         the comparison result is written back to a specified storage         address in the memory (optionally the scratchpad memory or the         scalar register file);     -   an Equal operation instruction (EQ): according to the         instruction, the device may obtain the parameters of the         instruction, including the length of a vector, the starting         address of the two vectors, and the storage address of the         output vector, directly from the instruction or by accessing the         serial number of the register of a memory (optionally a         scratchpad memory or a scalar register file) provided by the         instruction, then read the data of the two vectors, and compare         the elements at all positions in the vectors in a vector         comparison operation unit; at the position of a row, if the         value of a previous vector is equal to the value of a subsequent         vector, the value of the comparison result vector at that         position is set to 1, otherwise it is set to 0; finally, the         comparison result is written back to a specified storage address         in the memory (optionally the scratchpad memory or the scalar         register file);     -   an Unequal operation instruction (UEQ): according to the         instruction, the device may obtain the parameters of the         instruction, including the length of a vector, the starting         address of the two vectors, and the storage address of the         output vector, directly from the instruction or by accessing the         serial number of the register of a memory (optionally a         scratchpad memory or a scalar register file) provided by the         instruction, then read the data of the two vectors, and compare         the elements at all positions in the vectors in a vector         comparison operation unit; at the position of a row, if the         value of a previous vector is unequal to the value of a         subsequent vector, the value of the comparison result vector at         that position is set to 1, otherwise it is set to 0; finally,         the comparison result is written back to a specified storage         address in the memory (optionally the scratchpad memory or the         scalar register file);     -   a Vector Max instruction (VMAX): according to the instruction,         the device fetches vector data of a specified size from a         specified address in a scratchpad memory of a memory (optionally         a scratchpad memory or a scalar register file), selects a         largest element from the vector data as a result, and writes the         result back; optionally, the result is written back to a         specified address in the scalar register file of the memory         (optionally a scratchpad memory or a scalar register file);     -   a Vector Min instruction (VMIN): according to the instruction,         the device fetches vector data of a specified size from a         specified address in a memory (optionally a scratchpad memory or         a scalar register file), selects a minimum element from the         vector data as a result, and writes the result back; optionally,         the result is written back to a specified address in the scalar         register file of the memory (optionally a scratchpad memory or a         scalar register file);     -   a Cyclic Shift operation instruction: according to the         instruction, the device may obtain parameters of the instruction         directly from the instruction or by accessing the serial number         of the register of a memory (optionally a scratchpad memory or a         scalar register file) provided by the instruction, then         cyclically shift vectors in a vector shift unit (which may be a         separate vector shift unit or a computation unit), and then         write the result of the shift back to a specified storage         address in the memory (optionally the scratchpad memory or the         scalar register file), where a format of the cyclic shift         operation instruction format may include four operation fields,         a starting address and length of a vector, a shift stride, and a         storage address of an output vector; and     -   a Random-Vector generation instruction: according to the         instruction, the device reads one or more randomly distributed         parameters, and the size and storage address of a random vector         to be generated from the instruction or from the register file         of a memory (optionally a scratchpad memory or a scalar register         file), generates the random vector that is in line with the         random distribution in a random vector generation unit, and then         writes the result of the random vector back to the specified         storage address in the memory (optionally the scratchpad memory         or the scalar register file).

The Random-Vector generation instruction may be:

-   -   a Uniform distribution instruction (UNIF): according to the         instruction, the device reads uniformly distributed upper and         lower bound parameters, and the size and storage address of the         random vector to be generated from the instruction or from the         register of a memory (optionally a scratchpad memory or a scalar         register file), generates the random vector that is in line with         the uniform distribution in a random vector generation unit, and         then writes the result of the random vector back to the         specified storage address in the memory (optionally the         scratchpad memory or the scalar register file); and     -   a Gaussian distribution instruction (GAUS): according to the         instruction, the device reads Gaussian distributed mean and         variance parameters, and the size and storage address of the         random vector to be generated from the instruction or from the         register of a memory (optionally a scratchpad memory or a scalar         register file), generates the random vector that is in line with         the Gaussian distribution in a random vector generation unit,         and then writes the result of the random vector back to the         specified storage address in the memory (optionally the         scratchpad memory or the scalar register file).

During execution of a convolutional neural network algorithm (a convolution operation instruction) by the computation device shown in FIG. 1A, please refer to the flowchart of the convolutional neural network algorithm shown in FIG. 1B. As shown in FIG. 1B, a convolutional neural network includes output data, an activation function, an input data layer, and a convolution kernel.

Each computation process includes: selecting corresponding input data x_(i) in the input data layer according to a convolution window, and then performing an addition operation on the input data and the convolution kernel. A computation process of the output data is s=s(Σwx_(i)+b) which is to multiply a convolution kernel w by input data x_(i), find the sum, add a bias b, and then perform an activation operation s(h) to obtain a final output data s. The multiplication of the convolution kernel and the input data is vector multiplication.

According to the size k_(x) of the convolution kernel on an X axis and the size k_(y) of the convolution kernel on the Y axis, the convolution window firstly selects input data of which the size is the same as that of the convolution kernel from the input data of which the size of the X axis is W and the size of the Y axis is H, performs horizontal translation and then vertical translation according to translation position vectors S_(x) and S_(y) of the convolution window, and traverses all the input data.

FIG. 1C shows a format of an instruction set according to an example of the present disclosure. As shown in the figure, a convolutional neural network operation instruction includes at least one opcode and at least one operation field. The opcode is for indicating a function of the convolutional neural network operation instruction. A convolutional neural network operation unit can perform a convolutional neural network operation by identifying the opcode. The operation field is for indicating data information of the convolutional neural network operation instruction. The data information may be an immediate operand or a register number (which, optionally, may be a register file), which includes a starting address and a length of input data, a starting address and a length of the convolution kernel, and a type of an activation function.

The instruction set includes: convolutional neural network COMPUTE instruction with different functions, a CONFIG instruction, an IO instruction, an NOP instruction, a JUMP instruction, and a MOVE instruction. The above operation instructions will not be further described herein. For details, please refer to related descriptions in the above examples.

Optionally, the instruction set may further include a convolution activation CONV_ACTIVATE instruction.

The convolution activation CONV_ACTIVATE instruction: according to the instruction, the device fetches input data and a convolution kernel of a specified size from a specified address in the scratchpad memory (optionally), performs a convolution operation in a convolution operation component, and then performs an activation function operation on an output result; the above-mentioned specified size may be set by the manufacturer or user.

In one example, the CONV_ACTIVATE instruction includes: a convolution operation instruction and an activation instruction. The activation instruction is configured to perform an activation function operation, and the convolution operation instruction is configured to perform a convolution operation. For details, please refer to related descriptions in the above examples.

FIG. 1D is a schematic structural diagram of a device for performing a convolutional neural network forward operation according to an example of the present disclosure. As shown in FIG. 3 , the device includes an instruction storage unit 1, a controller unit 2, a data access unit 3, an interconnection module 4, a primary operation module 5, and a plurality of secondary operation modules 6. The instruction storage unit 1, the controller unit 2, the data access unit 3, the interconnection module 4, the primary operation module 5, and the plurality of secondary operation modules 6 may all be realized in a form of a hardware circuit (for instance, including but not limited to FPGA, CGRA, ASIC, analog circuit, memristor, etc.).

The instruction storage unit 1 is configured to read an instruction through the data access unit 3 and store the instruction.

The controller unit 2 is configured to read an instruction from the instruction storage unit 1, decode the instruction into a control signal for controlling the behavior of other modules, and send the instruction to other modules such as the data access unit 3, the primary operation module 5, and the plurality of secondary operation modules 6.

The data access unit 3 can access an external address space, directly read and write data to each storage unit inside the device to complete the loading and storage of the data,

The interconnection module 4 is configured to connect the primary operation module and the secondary operation modules, and can be implemented into different interconnection topologies (such as tree structure, ring structure, grid structure, hierarchical interconnection, bus structure, etc.).

FIG. 1E schematically shows an implementation of the interconnection module 4: an H-tree module. The interconnection module 4 forms a data path between the primary operation module 5 and the plurality of secondary operation modules 6, where the data path is a binary tree path composed of a plurality of nodes. Each node can transfer data received from an upstream node to two downstream nodes, and merge data returned by the two downstream nodes and return to an upstream node. For instance, at the beginning of a computational phase of a convolutional neural network, neuron data in the primary operation module 5 is sent to each secondary operation module 6 through the interconnection module 4; when the secondary operation modules 6 finish computing, neuron values output by the respective secondary operation modules are spliced stage-by-stage into a complete vector composed of neurons in the interconnection module. For instance, if there are N secondary operation modules in the device, input data x_(i) is transferred to the N secondary operation modules and each of the secondary operation modules performs a convolution operation on the input data x_(i) and the convolution kernel corresponding to the secondary operation module to obtain scalar data. The scalar data of the respective secondary operation modules are merged into an intermediate vector including N elements by the interconnection module 4. If the convolution window obtains a total of A*B pieces of (A pieces in the X direction, B pieces in the Y direction, where X and Y are coordinate axes of the three-dimensional orthogonal coordinate system) input data x_(i) by traverse, a convolution operation is perform on the above A*B pieces of x_(i) and all the vectors obtained are merged in the primary operation module to obtain a three-dimensional intermediate result of A*B*N.

FIG. 1F is a block diagram of a structure of the primary operation module 5 of a device for performing a convolutional neural network forward operation according to an example of the present disclosure. As shown in FIG. 1F, the primary operation module 5 includes a first operation unit 51, a first data dependency determination unit 52, and a first storage unit 53.

The first operation unit 51 includes a vector addition unit 511 and an activation unit 512. The first operation unit 51 is configured to receive a control signal from the controller unit and complete various operational functions of the primary operation module 5. The vector addition unit 511 is configured to perform an operation of adding a bias in the forward computation of the convolutional neural network, and perform element-wise addition on bias data and the intermediate results to obtain a bias result. The activation operation unit 512 performs an activation function operation on the bias result. The bias data may be read in from an external address space, or may be stored locally.

The data dependency determination unit 52 is a port for the first operation unit 51 to read/write the first storage unit 53, so as to ensure consistency in reading data from and writing data to the first storage unit 53. At the same time, the first data dependency determination unit 52 is also configured to send data read from the first storage unit 53 to the secondary operation modules through the interconnection module 4. Output data of the secondary operation modules 6 is directly sent to the first operation unit 51 through the interconnection module 4. An instruction output by the controller unit 2 is sent to the operation unit 51 and the first data dependency determination unit 52 to control their behavior.

The storage unit 53 is configured to cache input data and output data used by the primary operation module 5 during a computation process.

FIG. 1G is a block diagram of a structure of the secondary operation modules 6 of a device for performing a convolutional neural network forward operation according to an example of the present disclosure. As shown in FIG. 1E, each secondary operation module 6 includes a second operation unit 61, a data dependency determination unit 62, a second storage unit 63, and a third storage unit 64.

The second operation unit 61 is configured to receive a control signal from the controller unit 2 and perform a convolution operation. The second operation unit includes a vector multiplication unit 611 and an accumulation unit 612, which are respectively responsible for a vector multiplication operation and an accumulation operation in a convolution operation.

The second data dependency determination unit 62 is responsible for reading and writing the second storage unit 63 during a computation process. Before performing read/write operations, the second data dependency determination unit 62 first ensures that there is no consistency conflict between the reading and writing of data used by instructions. For instance, all control signals sent to the data dependency unit 62 are stored in the instruction queue inside the data dependency unit 62. In this queue, if a range of data to be read by a reading instruction conflicts with a range of data to be written by a writing instruction that is located at the front of the queue, the instruction can only be executed until a writing instruction depended by the instruction has been executed.

The second storage unit 63 is configured to cache input data and output scalar data of the secondary operation modules 6.

The third storage unit 64 is configured to cache convolution kernel data required by the secondary operation modules 6 in a computation process.

FIG. 1H is a flowchart of executing a convolutional neural network by a convolutional neural network operation device according to an example of the present disclosure. As shown in FIG. 1H, a process of executing the convolutional neural network neural network instruction includes:

-   -   a step S1, pre-storing an IO instruction in a starting address         of the instruction storage unit 1;     -   a step S2, the operation starts, reading, by the controller unit         2, the IO instruction from the starting address of the         instruction storage unit 1, and according to a control signal         decoded from the instruction, reading, by the data access unit         3, all corresponding convolutional neural network operation         instructions from an external address space, and caching the         instructions in the instruction storage unit 1;     -   a step S3, reading, by the controller unit 2, a next IO         instruction from the instruction storage unit, and according to         a control signal obtained by decoding, reading, by the data         access unit 3, all data (such as input data, an interpolation         table for a quick activation function operation, a constant         table for configuring parameters of the operation device, bias         data, etc.) required by the primary operation module 5 from the         external address space to the first storage unit 53 of the         primary operation module 5;     -   a step S4, reading, by the controller unit 2, a next IO         instruction from the instruction storage unit, and according to         a control signal decoded from the instruction, reading, by the         data access unit 3, convolution kernel data required by the         secondary operation modules 6 from the external address space;     -   a step S5, reading, by the controller unit 2, a next CONFIG         instruction from the instruction storage unit, and according to         a control signal obtained by decoding, configuring, by the         device, various constants required by the computation of the         neural network layer; for instance, the first operation unit 51         and the second operation unit 61 may configure a value of an         internal register of the parameter configuration unit in the         control signal, where the parameter includes, for instance, data         required by an activation function;     -   a step S6, reading, by the controller unit 2, a next COMPUTE         instruction from the instruction storage unit, and according to         a control signal decoded from the instruction, sending, by the         primary operation module 5, input data in a convolution window         to each secondary operation module 6 through an interconnection         module 4 and saving the input data to the second storage unit 63         of the secondary operation module 6; and then moving the         convolution window according to the instruction;     -   a step S7, according to the control signal decoded from the         COMPUTE instruction, reading, by the operation unit 61 of the         secondary operation module 6, the convolution kernel from the         third storage unit 64; reading the input data from the second         storage unit 63 to complete the convolution operation of the         input data and the convolution kernel; and returning an obtained         intermediate result through the interconnection module 4;     -   a step S8, in the interconnection module 4, splicing         intermediate results returned from respective secondary         operation modules 6 stage by stage to obtain a complete         intermediate vector;     -   a step S9, obtaining, by the primary operation module 5, the         intermediate vector returned by the interconnection module 4;         traversing, by the convolution window, all input data; splicing,         by the primary operation module, all returned vectors into an         intermediate result; according to the control signal decoded         from the COMPUTE instruction, reading bias data from the first         storage unit 53, adding the intermediate result and the bias         data in a vector addition unit 511 to obtain a bias result;         activating the bias result by the activation unit 512, and         writing final output data back to the first storage unit; and     -   a step S10, reading, by the controller unit 2, a next IO         instruction from the instruction storage unit, and according to         a control signal decoded from the instruction, storing, by the         data access unit 3, the output data in the first storage unit 53         to a specified address in the external address space, then the         operation finishes.

The implementation of a multi-layer convolutional neural network is similar to that of a single-layer convolutional neural network. After an upper layer of the convolutional neural network is executed, an operation instruction of a next layer uses an output data address of the upper layer stored in the primary operation unit as an input data address of this layer. Similarly, an address of a convolution kernel and an address of bias data in the instruction may also be changed to an address corresponding to this layer.

The present disclosure uses a device and an instruction set for performing the convolutional neural network forward operation, which solves the problem of the lack of CPU and GPU computation performance, and the problem of high front-end decoding overhead. The present disclosure effectively improves support for the forward operation of a multi-layer convolutional neural network.

By using a dedicated on-chip cache for the forward operation of a multi-layer convolutional neural network, input neurons and convolution kernel may be fully reused, which may avoid repeated reading of these data from the memory, reduce the memory access bandwidth, and prevent the memory bandwidth from becoming a performance bottleneck of the forward operation of a multi-layer artificial neural network.

Based on the above examples, FIG. 2 shows an information processing method according to an example of the present disclosure. The method shown in FIG. 2 may include:

-   -   a step S102, obtaining, by the computation device, a target         image to be processed, where the target image includes a target         object to be identified;

The target image carries/includes a target object to be identified, where the target object includes, but is not limited to, commodities, people, animals, etc., such as milk and water glasses. A count of the target objects and an input format of the target image are not limited herein. For instance, there may be one or more target objects, the target image may be an image containing one or more key features, or be one or more segments of videos containing key features. The key features are the features of the target object, such as a name or a shape of the object, and the like.

The method further includes: a step S104, using, by the computation device, the target image as input of the target image to call an operation instruction to perform object detection and identification on the target image, so as to obtain a target identification result, where the target detection result is used to indicate the target object in the target image, and the operation instruction is a pre-stored instruction for object detection and identification.

The operation instruction includes, but is not limited to, a convolution operation instruction, a pooling instruction, a normalization instruction, a non-linear activation instruction, and the like. For details, please refer to related descriptions in the above examples of FIG. 1 . Optionally, the process of calling related operation instructions in the computation device (such as an operation unit) to perform object detection and classification processing on the target image will not be further described herein. For details, please refer to the specific descriptions of calling related instruction in the above examples of FIG. 1 .

Some examples involved in the present disclosure are described below.

In the step S102, the input format of the target image may be an image format such as bmp, gif, jpeg, etc.; or may also be multi-dimensional matrix data converted from pixels of the image.

In an optional example, the step S102 specifically includes: obtaining an original image to be processed and pre-processing the original image to obtain the target image to be processed, where the pre-processing includes one or more of the following processing: format conversion, color conversion, and image restoration.

In a specific implementation, the computation device obtains an original image to be processed input by a user. The description of the original image will not be further described herein. For details, please refer to the related description of the target image. Further, the computation device may call related operation instructions to perform pre-processing, such as normalization, format conversion, color conversion, etc., on the original image to obtain the target image to be processed. The pre-processing includes, but is not limited to, format conversion (such as normalization processing and the like), color conversion (such as converting into a gray-scale image), image restoration, image modification, and other operation processing. Correspondingly, the operation instruction may be an instruction related to the pre-processing. For instance, when the pre-processing is the normalization processing, the corresponding operation instruction is a normalization instruction.

The pre-processing includes, but is not limited to, any one or more of the following: data format conversion (such as normalization, integer data conversion, etc.), data deduplication, data exception, filling missing data, color conversion, image restoration, image modification, and the like. For instance, the data format conversion may specifically be: conversion between continuous data and discrete data; power conversion, which is to convert non-power weight data in input data (a multi-dimensional matrix of the target image) of a neural network to power weight data; statistics of floating-point data, which is to count bits of exponent offset and exponent bits required for storing different types of data during a forward operation of the artificial neural network; and floating-point data conversion, which is to convert between a short-bit floating point data type and a long-bit floating point data type, and the like, which is not limited in the present disclosure.

A specific implementation of the step S104 may be: using, by the computation device, the target image as input of the operation unit to call a first operation instruction to perform feature extraction on the target image, thereby obtaining a feature image; further, calling a second operation instruction to perform object detection and identification on the feature image, thereby obtaining a target detection result, where the first operation instruction is a preset instruction for feature extraction, and the second operation instruction is a preset instruction for object detection and identification.

Firstly, some examples involved in the feature extraction are described below.

Specifically, the computation device may call the related first operation instruction in the operation unit to perform feature extraction on the target image to obtain a feature image. It should be understood that when an expression form of the target image is a multi-dimensional matrix, feature extraction of the target image belongs to data dimensionality reduction, which reduces the complexity of data processing, reduces the computation load of the computation device, and improves computation efficiency.

In an optional example, the first operation instruction may be an instruction for feature extraction. For details, please refer to related descriptions in the above examples.

In an optional example, the first operation instruction may include any one or more of the following instructions: a convolution operation instruction, a normalization instruction, a non-linear activation instruction, and a pooling instruction. It should be noted that when there are a plurality of the first operation instructions, which can also be called an operation instruction set, an order, count, and calling thread of the respective first operation instructions in the operation instruction set may be customized by the user side or the computer device side (such as a terminal), which is not limited herein.

FIG. 3 shows a schematic diagram of calling an operation instruction based on single-thread to perform feature extraction. Specifically, the controller unit may fetch a convolution operation instruction from the register unit and send the convolution operation instruction to the operation unit to process the target image, thereby obtaining a first intermediate image. Then the controller unit may fetch a normalization instruction from the register unit and send the normalization instruction to the operation unit to process the first intermediate image, thereby obtaining a second intermediate image. The controller unit may fetch a non-linear activation instruction from the register unit and send the non-linear activation instruction to the operation unit to process the second intermediate image, thereby obtaining a third intermediate image. Then the controller unit may fetch a pooling instruction from the register unit and send the pooling instruction to the operation unit to process the third intermediate image, thereby obtaining a feature image after feature fetchion.

Optionally, when each operation instruction shown in FIG. 3 is called for execution, the execution order may be changed; for instance, the normalization instruction may be called before the convolution operation instruction, which is not limited herein.

In an optional example, the present disclosure supports multi-thread (multiple pipelines) feature extraction processing. In other words, the feature extraction in the present disclosure may be implemented by threads splitting or merging. Implementations of thread splitting include, but are not limited to, data copying, data grouping, and the like, while implementations of thread merging include, but are not limited to, data addition and subtraction, data multiplication, data combination and arrangement, and the like.

FIG. 4 shows a schematic diagram of calling an operation instruction based on multiple threads to perform feature extraction. Specifically, the computation device may perform data operations of two threads at the same time. The operation instructions to be used in each thread may be the same or different, and an order and a count of calling the operation instructions are not limited herein. As shown in FIG. 4 , one of the threads sequentially executes the operation instructions in FIG. 3 twice at the same time, while the other thread sequentially executes the operation instructions in FIG. 3 once.

It should be noted that when multi-thread feature extraction is involved in the present disclosure, the feature image after feature extraction may be obtained by aggregating result data processed by each thread. In other words, the feature image data after the feature extraction may include, but is not limited to, a plurality of pieces of matrix data with the same dimension or different dimensions, which is not limited herein.

In some possible examples, the computation device may also call other instructions related to a feature extraction algorithm to perform feature extraction on the target image, so as to obtain a feature image. The feature extraction algorithm includes, but is not limited to HOG (Histogram of Oriented Gradients), an SIFT feature extraction algorithm, etc., and these feature extraction algorithms are composed of corresponding one or more operation instructions, which are not limited in the present disclosure.

Secondly, some examples involved in the object detection and identification are described below.

Specifically, the computation device may call the second operation instruction to perform object detection and identification on the feature image to obtain a target detection result, the process of which is similar to that of the above feature extraction. In some possible examples, the target detection result includes target information corresponding to the target object, where the target information includes one or more of the following: a target position (or a target bounding box used to indicate a target position of the target object in the target image), a target classification, and a target score.

The target position is a position of the target object in the target image. Optionally, the target position may be represented by a target bounding box (minimum bounding box). For instance, the position may be represented by upper-left corner pixel coordinates, a width, and a height of a minimum bounding matrix, or be represented by center coordinates, a width, and a height of the minimum bounding matrix, or be represented by upper-left corner and lower-right corner pixel coordinates of the minimum bounding matrix, and the like. For instance, if a target image contains an image of a box of milk, the minimum bounding matrix is a matrix formed by smallest frames including the image of milk, and the matrix can be described to be represented by center coordinates, a height and a width of the image of milk.

The target classification is used to indicate a category/classification to which the target object belongs. The target classification can be identified by a corresponding preset character, a preset value (such as a index), a preset strings, etc., which is not limited herein. The target score is used to indicate an accuracy of the target bounding box bounding the target object; in other words, the target score is used to indicate whether the target bounding box encloses/bounds the target object and indicate the accuracy of bounding the target object. FIG. 5 shows respective scores of respective bounding boxes bounding a nose.

In a specific implementation, the computation device calls the second operation instruction to perform object detection and identification on the feature image (feature image data obtained after feature extraction), so as to obtain a plurality of detection results of the target image. Specifically, there may be n detection results, where n is a positive integer. The above process is similar to the above feature extraction process. Optionally, for details of the detection result, please refer to related descriptions of the target detection result.

When the detection result includes the bounding box, classification, and score corresponding to the target object, the computation device may call a related operation instruction (such as a vector operation instruction) to implement the non-maximum value suppression NMS algorithm, so as to select a detection result which satisfies a scoring condition from the n detection results as the target detection result corresponding to the target object. The scoring condition is customized by the user side or the computation device side, such as a highest score, a lowest score, a score exceeding a preset threshold, etc., which is not limited herein.

It should be noted that a processing similar to the feature extraction described above can be used to perform object detection and identification on the feature image, which is to perform object detection and identification on the feature image based on a single-thread or multi-thread operation instruction set. The operation instruction set may be shown in FIG. 3 and FIG. 4 . Optionally, after object detection and identification are performed on the feature image using the operation instruction set of each thread, the fully connected instruction may be further configured to aggregate processing result data of each thread to obtain the detection result for the target object.

In addition, the first operation instruction and the second operation instruction in the present application may be the same or different. For the operation instruction, please refer to the related descriptions in the above examples.

The examples of the present disclosure are briefly introduced below combined with the examples of FIGS. 1A-1H.

In the step S102, the computation device obtains a target image to be processed input by a user. In an optional example, the communication unit may be the storage medium (the off-chip memory) shown in FIG. 1A or may be an input/output (IO) unit, which is not limited herein.

In an optional example, the computation device may be the computation device shown in FIG. 1A or FIG. 1D. Specifically, the computation device can store various operation instructions in the register unit or the instruction storage unit through the data access unit; further, the computation device can read/write and store various operation instructions through the data access unit. The controller unit is configured to control the reading of various operation instructions from the register unit (or the instruction storage unit, etc.) and decode the operation instruction into an executable operation instruction. Optionally, the controller unit may also send the operation instruction to the operation unit for execution. Specifically, related arithmetic units can be called in turn for data processing according to the computation topology corresponding to the operation instruction. The convolution operation instruction is described in details below as an instance. The interconnection module is configured to receive input data (the target image) and a computation topology, where the computation topology is a topology corresponding to the operation instruction. For instance, when the operation instruction is a convolution operation instruction, the corresponding computation topology may be: the multiplication arithmetic unit—the addition arithmetic unit—(optional) the activation arithmetic unit. Each type of arithmetic unit is configured to perform a corresponding computational function operation, for instance, the multiplication arithmetic unit is configured to perform a multiplication operation, etc., which will not be further described in the present disclosure.

Other descriptions of the step S102 are similar to those of the above examples, which will not be further described herein.

Correspondingly, specific implementations of the step S104 are described below.

In a specific implementation, the computation device fetches a corresponding operation instruction from the register unit (or the instruction storage unit) through the controller unit and the data access unit, where the operation instruction is configured to process the target image (which may specifically be object identification processing). For the operation instruction, please refer to the related introduction in the above examples; for instance, the instruction may be an operation instruction for object detection and identification. The count of the operation instructions is not limited herein.

Further, after the controller unit fetches the operation instruction, the controller unit sends the operation instruction to the operation unit to perform object detection and identification on the target image in the operation unit according to the computation topology corresponding to the operation instruction, so as to obtain the target detection result.

A specific implementation process of the step S104 is described in detail below with the operation instruction being a convolution operation instruction as an instance.

In a specific implementation, referring to the computation device shown in FIG. 1A, the computation device obtains a first image to be processed input by a user through the communication unit (or a storage medium, or an off-chip memory). Optionally, the computation device may call a related computation instruction to perform conversion of a preset format on the first image, thereby obtaining image data which can be identified and processed by the computation device, such as a matrix or vector composed of i pieces of x_(i) pixel data. The preset format is customized by the user side or the computation device side. Further, the computation device fetches a convolution operation instruction from the register unit through the data access unit and the controller unit, and sends the convolution operation instruction to the operation unit for execution, in other words, a formula to be executed is s=s(Σwx_(i)+b) is convolution kernel, and x_(i) is input data. Correspondingly, the computation device controls the operation unit to execute the convolution operation instruction on the input data x_(i) (the first image). Specifically, the computation device calls the multiplication arithmetic unit in the operation unit to multiply a convolution kernel w by input data x_(i), calls the addition arithmetic unit to find the sum, adds a bias b, and then calls the activation arithmetic unit to perform an activation operation s(h) so as to obtain a final output result S. The output result is the target detection result or intermediate data. When the output result is intermediate data, according to a similar computation principle of the above convolution operation instruction, the computation device may further call other operation instructions to process the intermediate data. The process is repeated until the second image is obtained.

In another specific implementation, referring to the computation device shown in FIG. 1D, the process is similar to that of the above step S104 and uses the computation device shown in 1D. The operation unit may specifically include a primary operation module, secondary operation modules, and an interconnection module connecting the primary operation module and the secondary operation modules. The interconnection module may be configured to transfer data between the primary operation module and the secondary operation modules, receive a computation topology corresponding to an operation instruction, etc. The computation device may control an implementation of a bias b operation and an activation S (h) operation in the convolution operation in the primary operation module, and control an implementation of a vector multiplication operation wx_(i) and an accumulation operation Σ in the respective secondary operation modules. Specifically, the computation device may transfer input data x_(i) (the first image) to each secondary operation module through the controller unit, so as to first call a multiplication arithmetic unit in each secondary operation module to multiply a convolution kernel w by the input data x_(i), and then call an addition arithmetic unit to sum and obtain an output scalar. Then the interconnection module is configured to accumulate and splice output scalars of the respective secondary operation modules stage by stage into an intermediate vector and send the intermediate vector to the primary operation module. Further, the computation device calls the addition arithmetic unit in the primary operation module to splice intermediate vectors corresponding to all input data into an intermediate result, adds a bias b to the intermediate result, and then calls an activation arithmetic unit to perform an activation operation s(h) to obtain a final output result S.

For the implementation of calling related operation instructions in the computation device to process the first image, please refer to related descriptions of the above FIGS. 1A to 1H. In other words, the examples of the FIGS. 1A to 1H may also be correspondingly applied to the examples of the information processing method described in FIG. 2 , and will not be further described herein. It should be understood that the convolution operation instruction in the above description is only used as an instance to illustrate the convolution operation instruction calling and data processing of the convolution operation instruction, which is not a limitation; accordingly, when the operation instruction is another instruction instead of the convolution operation instruction, a related processing method similar to that of the convolution operation instruction may also be used to implement steps of the method examples of the present disclosure.

The examples of the present disclosure may realize accurate, fast, and comprehensive detection and identification on a target object to be identified in a target image, which may have technical effects of lower power consumption and faster speed compared with the prior art in which a general-purpose processor is used for object detection and identification.

FIG. 6 is a structural diagram of a computation device (which may specifically be a terminal device) according to an example of the present disclosure. The computation device shown in FIG. 6 includes a communication unit 617 and an operation unit 614, where

-   -   the communication unit 617 is configured to obtain a target         image to be processed and input the target image to the         operation unit, where the target image includes a target object         to be identified;     -   the operation unit 614 is configured to receive the target image         and call an operation instruction to perform object detection         and identification on the target image, so as to obtain a target         detection result, where     -   the target detection result is used to indicate the target         object in the target image, and the operation instruction is a         pre-stored instruction for target object detection and         identification.

Optionally, the computation device further includes a storage medium 611 (optional), a register unit 612, an interconnection module 613, a controller unit 615, and a data access unit 616. For the above function units, please refer to related descriptions of the examples in FIG. 1 . Optionally, the communication unit and the storage medium may be the same or different. For instance, the communication unit may be a storage medium or be an IO unit of the computation device, which is not limited herein.

In an optional example, the computation device further includes a register unit and a controller unit, where

-   -   the computation device controls the controller unit to fetch a         first operation instruction and a second operation instruction         from the register unit, and send the first operation instruction         and the second operation instruction to the operation unit;     -   the operation unit is configured to call the first operation         instruction to perform feature extraction on the target image to         obtain a feature image; and     -   the operation unit is configured to call the second operation         instruction to perform object detection and identification on         the feature image, so as to obtain a target detection result,         where     -   the first operation instruction is a preset instruction for         feature extraction, and the second operation instruction is a         preset instruction for object detection and identification.

In some possible examples, the target detection result includes target information corresponding to the target object, and the target information includes at least one of the following: a target bounding box, a target classification, and a target score, where

-   -   the target bounding box includes the target object and is used         to indicate a position of the target object in the target image,         the target classification is a classification in which the         target object is located, and the target score is used to         indicate an accuracy of the target bounding box bounding the         target object.

In some possible examples, the target detection result includes: a target bounding box, a target classification, and a target score,

-   -   the operation unit is configured to call the second operation         instruction to perform object detection and identification on         the feature image, so as to obtain n detection results for the         target object, where the detection results include a rounding         box, a classification, and a score corresponding to the target         object; and     -   the operation unit is configured to select a detection result         with a highest score from the n detection results as the target         detection result.

In an optional example,

-   -   the operation unit is configured to obtain an original image to         be processed input by a user;     -   the operation unit is configured to pre-process the original         image to obtain the target image to be processed, where the         pre-processing includes one or more of the following processing:         format conversion, normalization, color conversion, and image         restoration.

In an optional example, the computation device further includes a data access unit and a storage medium,

-   -   the operation unit is configured to send the target detection         result to the data access unit and store the result in the         storage medium.

In an optional example, the operation unit includes a primary operation module and a plurality of secondary operation modules, where the primary operation module is interconnected with the plurality of secondary operation modules by an interconnection module, and when the operation instruction is a convolution operation instruction,

-   -   the secondary operation modules are configured to implement a         convolutional operation of input data and convolution kernels in         a convolution neural network algorithm, wherein the input data         is the target image and the convolution neural network algorithm         corresponds to the convolutional operation instruction,     -   the interconnection module is configured to implement data         transfer between the primary operation module and the secondary         operation modules; before a forward operation of a neural         network fully connected layer starts, the primary operation         module sends the input data to each secondary operating through         the interconnection module; and after the computation of the         secondary operation modules is completed, the interconnection         module splices output scalars of the respective secondary         operation modules stage by stage into an intermediate vector and         sends the intermediate vector back to the primary operation         module, and     -   the primary operation module is configured to splice         intermediate vectors corresponding to all input data into an         intermediate result, and perform subsequent operations on the         intermediate result, where

In an optional example,

-   -   the primary operation module is configured to add bias data to         the intermediate result, and then perform an activation         operation.

In an optional example, the primary operation module includes a first operation unit, where the first operation unit includes a vector addition unit and an activation unit,

-   -   the vector addition unit is configured to implement a bias         addition operation of a convolutional neural network operation         and perform element-wise addition on bias data and the         intermediate result to obtain a bias result; and     -   the activation unit is configured to perform an activation         function operation on the bias result.

In an optional example, the primary operation module includes a first storage unit, a first operation unit, a first data dependency determination unit, and a first storage unit, where

-   -   the first storage unit is configured to cache input data and         output data, output data used by the primary operation module         during a computation process, where the output data includes the         object detection result;     -   the first operation unit is configured to perform various         operational functions of the primary operation module,

The data dependency determination unit is configured to ensure that there is no consistency conflict in reading data from and writing data to the first storage unit, read an input neuron vector from the first storage unit, and send the vector to the secondary operation modules through the interconnection module; and

-   -   sending an intermediate result vector from the interconnection         module to the first operation unit.

In an optional example, the secondary operation modules include a second operation unit, where the second operation unit includes a vector multiplication unit and an accumulation unit,

-   -   the secondary operation modules are configured to perform a         convolution operation of input data and a convolution kernel in         a convolution neural network algorithm, which includes:     -   the vector multiplication unit is configured to perform a vector         multiplication operation of a convolution operation, and     -   the accumulation unit is configured to perform an accumulation         operation of the convolution operation.

In an optional example, each secondary operation module includes a second operation unit, a second data dependency determination unit, a second storage unit, and a third storage unit;

-   -   the second operation unit is configured to perform various         arithmetic and logical operations of the secondary operation         modules,     -   the second data dependency determination unit is configured to         perform a reading/writing operation on the second storage unit         and the third storage unit during a computation process to         ensure that there is no consistency conflict between the reading         and writing operations on the second storage unit and the third         storage unit,     -   the second storage unit is configured to cache input data and an         output scalar obtained from the computation performed by the         secondary operation module, and     -   the third storage unit is configured to cache a convolution         kernel required by the secondary operation module in the         computation process.

In an optional example, the first data dependency or the second data dependency ensures that there is no consistency conflict in reading and writing in the following manners: storage addresses corresponding to data/instructions stored in the corresponding storage unit do not overlap; or determining whether there is dependency between a control signal that has not been executed and data of a control signal that is being executed; if there is no dependency, the control signal is allowed to be issued immediately; otherwise, the control signal is not allowed to be issued until all control signals on which the control signal is dependent have been executed, where

-   -   the computation device controls the controller unit to obtain an         operation instruction from the register unit and decode the         operation instruction into the control signal for controlling         behavior of other modules, where the other modules include the         main the primary operation module and the plurality of of         secondary operation modules.

In an optional example, the plurality of secondary operation modules are configured to compute respective output scalars in parallel by configuration using the same input data and respective convolution kernels.

In an optional example, an activation function active used by the primary operation module may be any of the following non-linear functions: sigmoid, tanh, relu, softmax, or may be a linear function.

In an optional example, the interconnection module forms a data channel for continuous or discrete data between the primary operation module and the plurality of secondary operation modules. The interconnection module has any of the following structures: a tree structure, a ring structure, a grid structure, a hierarchical interconnection, and a bus structure.

For those parts which are not shown or described in the examples of the present disclosure, please refer to related descriptions of the above examples.

An example of the present disclosure further provides a computer storage medium on which a computer program is stored for electronic data exchange. The computer program may cause a computer to perform part or all of the steps of any information processing method described in the foregoing method examples.

An example of the present disclosure further provides a computer program product, where the computer program product includes a non-transitory computer-readable storage medium storing a computer program. The computer program may cause a computer to perform part or all of the steps of any information processing method described in the foregoing method examples.

An example of the present disclosure also provides an acceleration device which includes: a memory which stores executable instructions, and a processor configured to execute the executable instructions in the storage unit according to the information processing method.

The processing unit may be a single one, or may include two or more processing units. In addition, the processor may also include a general-purpose processor (CPU) or a graphics processing unit (GPU), a field programmable gate array (FPGA), or an application-specific integrated circuit (ASIC) to set up and operate a neural network.

The processor may also include an on-chip memory for caching (including a memory in the processing device).

In some examples, the present disclosure provides a chip which includes the above neural network processor configured to execute the information processing method.

In some examples, the present disclosure provides a chip package structure which includes the above chip.

In some examples, the present disclosure provides a board card which includes the above chip package structure.

In some examples, the present disclosure provides an electronic device which includes the above board card.

The electronic device may include a data processing device, a robot, a computer, a printer, a scanner, a tablet, a smart terminal, a mobile phone, a traffic recorder, a navigator, a sensor, a webcam, a server, a cloud-based server, a camera, a video camera, a projector, a watch, a headphone, a mobile storage, a wearable device, a vehicle, a household appliance, and/or a medical equipment.

The vehicle may include an airplane, a ship, and/or a car. The household electrical appliance may include a television, an air conditioner, a microwave oven, a refrigerator, an electric rice cooker, a humidifier, a washing machine, an electric lamp, a gas cooker, and a range hood. The medical equipment may include a nuclear magnetic resonance spectrometer, a B-ultrasonic scanner, and/or an electrocardiograph.

It should be noted that, the foregoing examples of method, for the sake of conciseness, are all described as a series of action combinations, but those skilled in the art should know that since according to the present disclosure, the steps may be performed in a different order or simultaneously, the disclosure is not limited by the described order of action. Secondly, those skilled in the art should also understand that the examples described in the specification are all optional, and the actions and modules involved are not necessarily required for this disclosure.

In the examples above, the description of each example has its own emphasis. For a part that is not described in detail in one example, reference may be made to related descriptions in other examples.

It should be understood that in the examples provided by the present disclosure, the disclosed device may be implemented in another manner. For instance, the examples above are merely illustrative. For instance, the division of the units is only a logical function division. In a real implementation, there may be another manner for division. For instance, a plurality of units or components may be combined or may be integrated in another system, or some features can be ignored or not performed. In addition, the displayed or discussed mutual coupling or direct coupling or communication connection may be implemented through indirect coupling or communication connection of some interfaces, devices or units, and may be electrical or other forms.

The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units. In other words, the components may be located in one place, or may be distributed to a plurality of network units. According to certain needs, some or all of the units can be selected for realizing the purposes of the examples of the present disclosure.

In addition, the functional units in each example of the present application may be integrated into one processing unit, or each of the units may exist separately and physically, or two or more units may be integrated into one unit. The integrated units above may be implemented in the form of hardware or in the form of software program modules.

When the integrated units are implemented in the form of a software program module and sold or used as an independent product, they may be stored in a computer-readable memory. Based on such understanding, the essence of the technical solutions of the present disclosure, or a part of the present disclosure that contributes to the prior art, or all or part of technical solutions, can all or partly embodied in the form of a software product that is stored in a memory. The software product includes several instructions to enable a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the examples of the present disclosure. The foregoing memory includes: a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk, or an optical disc, and other media that can store program codes.

A person of ordinary skill in the art may understand that all or part of the steps of the foregoing examples of method may be completed by a program instructing related hardware. The program may be stored in a computer-readable memory, and the memory may include a flash disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, an optical disk, or the like.

The examples of the present disclosure have been described in detail above. Specific examples have been used in the specification to explain the principles and implementation manners of the present disclosure. The descriptions of the above examples are only used to facilitate understanding of the methods and core ideas of the present disclosure. Persons of ordinary skill in the art may change the implementation and application scope according to the ideas of the present application. In summary, the content of this specification should not be construed as a limitation on the present disclosure. 

What is claimed is:
 1. An information processing method applied to a computation circuit, wherein the computation circuit includes a communication circuit and an operation circuit, and the method comprises: controlling, by the computation circuit, the communication circuit to obtain a target image to be processed, wherein the target image includes a target object to be identified; and controlling, by the computation circuit, the operation circuit to obtain and call an operation instruction to perform object detection and identification on the target image, so as to obtain a target detection result, wherein the target detection result is used to indicate the target object in the target image, and the operation instruction is a pre-stored instruction for object detection and identification, wherein the operation circuit includes a primary operation circuit and a plurality of secondary operation circuits, wherein the primary operation circuit is interconnected with the plurality of secondary operation circuits by an interconnection circuit, and when the operation instruction is a convolution operation instruction, the calling the operation instruction to perform object detection and identification on the target image includes: controlling, by the computation circuit, the secondary operation circuits to parallelly implement a convolution operation of input data and a convolution kernel in a convolutional neural network algorithm, wherein the input data is the target image and the convolutional neural network algorithm corresponds to the convolution operation instruction, controlling, by the computation circuit, the interconnection circuit to implement data transfer between the primary operation circuit and the secondary operation circuits, before a forward operation of a neural network fully connected layer starts, transferring, by the primary operation circuit, the input data to each secondary operation circuit through the interconnection circuit, and after the computation of the secondary operation circuits is completed, splicing, by the interconnection circuit, output scalars of the respective secondary operation circuits stage by stage to obtain an intermediate vector, and sending the intermediate vector back to the primary operation circuit, controlling, by the computation circuit, the primary operation circuit to splice intermediate vectors corresponding to all input data into an intermediate result for subsequent operations, and controlling, by the computation circuit, the primary operation circuit to add bias data to the intermediate result, and performing an activation operation.
 2. The method of claim 1, wherein the computation circuit further includes a register circuit and a controller circuit, and the controlling, by the computation circuit, the operation circuit to obtain an operation instruction to perform object detection and identification on the target image, so as to obtain a target detection result includes: controlling, by the computation circuit, the controller circuit to fetch a first operation instruction and a second operation instruction from the register circuit, and sending, by the computation circuit, the first operation instruction and the second operation instruction to the operation circuit, controlling, by the computation circuit, the operation circuit to call the first operation instruction to perform feature extraction on the target image to obtain a feature image, and controlling, by the computation circuit, the operation circuit to call the second operation instruction to perform object detection and identification on the feature image, so as to obtain a target detection result, wherein the first operation instruction is a preset instruction for feature extraction, and the second operation instruction is a preset instruction for object detection and identification.
 3. The method of claim 2, wherein the target detection result includes target information corresponding to the target object, and the target information includes at least one of the following: a target bounding box, a target classification, and a target score, wherein the target bounding box includes the target object and is used to indicate a position of the target object in the target image, the target classification is a classification to which the target object belongs, and the target score is used to indicate an accuracy of the target bounding box bounding the target object.
 4. The method of claim 3, wherein the target detection result includes: a target bounding box, a target classification, and a target score, and the calling the operation instruction to perform object detection and identification on the target image, so as to obtain a target detection result includes: controlling, by the computation circuit, the operation circuit to call the second operation instruction to perform object detection and identification on the feature image, so as to obtain n detection results for the target object, wherein the detection results include a rounding box, a classification, and a score corresponding to the target object, and controlling, by the computation circuit, the operation circuit to select a detection result with a highest score from the n detection results as the target detection result.
 5. The method of claim 1, wherein the controlling, by the computation circuit, the communication circuit to obtain a target image to be processed includes: controlling, by the computation circuit, the communication circuit to obtain an original image to be processed input by a user, and controlling, by the computation circuit, the operation circuit to pre-process the original image to obtain a target image to be processed, wherein the pre-processing includes one or more of the following processing: format conversion, normalization, color conversion, and image restoration.
 6. The method of claim 1, wherein the computation circuit further includes a data access circuit and a storage medium, and the computation circuit controls the operation circuit to send the target detection result to the data access circuit and store the result in the storage medium.
 7. The method of claim 1, wherein the computation circuit controls the plurality of secondary operation circuits to compute respective output scalars in parallel by using the same input data and respective convolution kernels.
 8. The method of claim 1, wherein the primary operation circuit includes a first operation circuit, wherein the first operation circuit includes a vector addition circuit and an activation circuit, the controlling, by the computation circuit, the primary operation circuit to add bias data to the intermediate result, and then performing an activation operation include: controlling, by the computation circuit, the vector addition circuit to implement a bias addition operation of a convolutional neural network operation and perform element-wise addition on bias data and the intermediate result to obtain a bias result, and controlling, by the computation circuit, the activation circuit to perform an activation function operation on the bias result.
 9. The method of claim 1, wherein each secondary operation circuit includes a second operation circuit, wherein the second operation circuit includes a vector multiplication circuit and an accumulation circuit, and the method includes: controlling, by the computation circuit, the secondary operation circuit to perform a convolution operation of input data and a convolution kernel in a convolutional neural network algorithm, controlling, by the computation circuit, the vector multiplication circuit to perform a vector multiplication operation of the convolution operation, and controlling, by the computation circuit, the accumulation circuit to perform an accumulation operation of the convolution operation.
 10. A computation circuit, comprising a communication circuit and an operation circuit, wherein the communication circuit is configured to obtain a target image to be processed, wherein the target image includes a target object to be identified; and the operation circuit is configured to call an operation instruction to perform object detection and identification on the target image, so as to obtain a target detection result, wherein the target detection result is used to indicate the target object in the target image, and the operation instruction is a pre-stored instruction for object detection and identification, wherein the operation circuit includes a primary operation circuit and a plurality of secondary operation circuits, wherein the primary operation circuit is interconnected with the plurality of secondary operation circuits by an interconnection circuit, and when the operation instruction is a convolution operation instruction, the secondary operation circuits are configured to parallelly implement a convolutional operation of input data and convolution kernels in a convolution neural network algorithm, wherein the input data is the target image and the convolution neural network algorithm corresponds to the convolutional operation instruction, the interconnection circuit is configured to implement data transfer between the primary operation circuit and the secondary operation circuits, before a forward operation of a neural network fully connected layer starts, the primary operation circuit sends the input data to each secondary operation circuit through the interconnection circuit, and after the computation of the secondary operation circuits is completed, the interconnection circuit splices output scalars of the respective secondary operation circuits stage by stage into an intermediate vector and sends the intermediate vector back to the primary operation circuit, and the primary operation circuit is configured to splice intermediate vectors corresponding to all input data into an intermediate result, and perform subsequent operations on the intermediate result, wherein the primary operation circuit is configured to add bias data to the intermediate result, and then perform an activation operation.
 11. The computation circuit of claim 10, further comprising a register circuit and a controller circuit, wherein the controller circuit is configured to fetch a first operation instruction and a second operation instruction from the register circuit, and send the first operation instruction and the second operation instruction to the operation circuit, the operation circuit is configured to call the first operation instruction to perform feature extraction on the target image to obtain a feature image, and the operation circuit is configured to call the second operation instruction to perform object detection and identification on the feature image, so as to obtain a target detection result, wherein the first operation instruction is a preset instruction for feature extraction, and the second operation instruction is a preset instruction for object detection and identification.
 12. The computation circuit of claim 11, wherein the target bounding box includes target information corresponding to the target object, wherein the target information includes at least one of the following: a target bounding box, a target classification, and a target score, wherein the target bounding box includes the target object and is used to indicate a position of the target object in the target image, the target classification is a classification to which the target object belongs, and the target score is used to indicate an accuracy of the target bounding box bounding the target object, when the target detection result includes a target bounding box, a target classification, and a target score, the operation circuit is configure to call the second operation instruction to perform object detection and identification on the feature image, so as to obtain n detection results for the target object, wherein the detection results include a rounding box, a classification, and a score corresponding to the target object, and then the operation circuit is configured to select a detection result with a highest score from the n detection results as the target detection result.
 13. The computation circuit of claim 10, wherein the communication circuit is configured to obtain an original image to be processed input by a user, the operation circuit is configured to pre-process the original image to obtain the target image to be processed, wherein the pre-processing includes one or more of the following processing: format conversion, normalization, color conversion, and image restoration.
 14. The computation circuit of claim 10, wherein the plurality of secondary modules circuits use the same input data and respective convolution kernels to compute respective output scalars in parallel. 